SoroushVahidi/scidocs huggingface dataset:
Mixtures of hierarchical topics with Pachinko allocation
(2007)

The four-level pachinko allocation model (PAM) (Li & McCallum, 2006) represents correlations among topics using a DAG structure. It does not, however, represent a nested hierarchy of topics, with some topical word distributions representing the vocabulary that is shared among several more specific topics. This paper presents hierarchical PAM---an enhancement that explicitly represents a topic hierarchy. This model can be seen as combining the advantages of hLDA's topical hierarchy representation with PAM's ability to mix multiple leaves of the topic hierarchy. Experimental results show improvements in likelihood of held-out documents, as well as mutual information between automatically-discovered topics and humangenerated categories such as journals.
A Condensation Approach to Privacy Preserving Data Mining
(2004)

In recent years, privacy preserving data mining has become an important problem because of the large amount of personal data which is tracked by many business applications. In many cases, users are unwilling to provide personal information unless the privacy of sensitive information is guaranteed. In this paper, we propose a new framework for privacy preserving data mining of multi-dimensional data. Previous work for privacy preserving data mining uses a perturbation approach which reconstructs data distributions in order to perform the mining. Such an approach treats each dimension independently and therefore ignores the correlations between the different dimensions. In addition, it requires the development of a new distribution based algorithm for each data mining problem, since it does not use the multi-dimensional records, but uses aggregate distributions of the data as input. This leads to a fundamental re-design of data mining algorithms. In this paper, we will develop a new and flexible approach for privacy preserving data mining which does not require new problem-specific algorithms, since it maps the original data set into a new anonymized data set. This anonymized data closely matches the characteristics of the original data including the correlations among the different dimensions. We present empirical results illustrating the effectiveness of the method.
Nonlinear dimensionality reduction by locally linear embedding.
(2000)

Many areas of science depend on exploratory data analysis and visualization. The need to analyze large amounts of multivariate data raises the fundamental problem of dimensionality reduction: how to discover compact representations of high-dimensional data. Here, we introduce locally linear embedding (LLE), an unsupervised learning algorithm that computes low-dimensional, neighborhood-preserving embeddings of high-dimensional inputs. Unlike clustering methods for local dimensionality reduction, LLE maps its inputs into a single global coordinate system of lower dimensionality, and its optimizations do not involve local minima. By exploiting the local symmetries of linear reconstructions, LLE is able to learn the global structure of nonlinear manifolds, such as those generated by images of faces or documents of text.
Influence of External Currents in Sensors Based on PCB Rogowski Coils
(2005)

A current sensor based in the Rogowski coil is an innovative measuring system that gives advantages with respect to conventional measuring systems based in current transformers with magnetic core [1], [2].and [3]. Their main advantages are: the linearity, the span and the bandwidth. Different kinds of manufacturing allow to obtain an important variety of Rogowski coils with different properties. One way of manufacturing is using the same method as for producing printed circuit boards, so by this way is possible to produce coils very similar and with a high precision. The authors are working in current measurement with Rogowski coils or Hall effect sensors and in particular in the realization of good and accurate coils [4] and [5]. In this work, the influence of external currents to the coil in the measured current by the coil has been evaluated.
Identifying quantitative trait loci via group-sparse multitask regression and feature selection: an imaging genetics study of the ADNI cohort
(2012)

MOTIVATION
Recent advances in high-throughput genotyping and brain imaging techniques enable new approaches to study the influence of genetic variation on brain structures and functions. Traditional association studies typically employ independent and pairwise univariate analysis, which treats single nucleotide polymorphisms (SNPs) and quantitative traits (QTs) as isolated units and ignores important underlying interacting relationships between the units. New methods are proposed here to overcome this limitation.


RESULTS
Taking into account the interlinked structure within and between SNPs and imaging QTs, we propose a novel Group-Sparse Multi-task Regression and Feature Selection (G-SMuRFS) method to identify quantitative trait loci for multiple disease-relevant QTs and apply it to a study in mild cognitive impairment and Alzheimer's disease. Built upon regression analysis, our model uses a new form of regularization, group ℓ(2,1)-norm (G(2,1)-norm), to incorporate the biological group structures among SNPs induced from their genetic arrangement. The new G(2,1)-norm considers the regression coefficients of all the SNPs in each group with respect to all the QTs together and enforces sparsity at the group level. In addition, an ℓ(2,1)-norm regularization is utilized to couple feature selection across multiple tasks to make use of the shared underlying mechanism among different brain regions. The effectiveness of the proposed method is demonstrated by both clearly improved prediction performance in empirical evaluations and a compact set of selected SNP predictors relevant to the imaging QTs.


AVAILABILITY
Software is publicly available at: http://ranger.uta.edu/%7eheng/imaging-genetics/.
Security Against Hardware Trojan Attacks Using Key-Based Design Obfuscation
(2011)

Malicious modification of hardware in untrusted fabrication facilities, referred to as hardware Trojan, has emerged as a major security concern. Comprehensive detection of these Trojans during postmanufacturing test has been shown to be extremely difficult. Hence, it is important to develop design techniques that provide effective countermeasures against hardware Trojans by either preventing Trojan attacks or facilitating detection during test. Obfuscation is a technique that is conventionally employed to prevent piracy of software and hardware intellectual property (IP). In this work, we propose a novel application of key-based circuit structure and functionality obfuscation to achieve protection against hardware Trojans triggered by rare internal circuit conditions. The proposed obfuscation scheme is based on judicious modification of the state transition function, which creates two distinct functional modes: normal and Responsible Editor: S. T. Chakradhar A preliminary version of this work has been published in the International Conference on Computer Aided Design (ICCAD), 2009. R. S. Chakraborty (B) Department of Computer Science and Engineering, Indian Institute of Technology, Kharagpur, West Bengal 721302, India e-mail: rschakraborty@cse.iitkgp.ernet.in S. Bhunia Department of Electrical Engineering and Computer Science, Case Western Reserve University, Cleveland, OH 44106, USA e-mail: skb21@case.edu obfuscated. A circuit transitions from the obfuscated to the normal mode only upon application of a specific input sequence, which defines the key. We show that it provides security against Trojan attacks in two ways: (1) it makes some inserted Trojans benign, i.e. they become effective only in the obfuscated mode; and (2) it prevents an adversary from exploiting the true rare events in a circuit to insert hard-to-detect Trojans. The proposed design methodology can thus achieve simultaneous protection from hardware Trojans and hardware IP piracy. Besides protecting ICs against Trojan attacks in foundry, we show that it can also protect against malicious modifications by untrusted computeraided design (CAD) tools in both SoC and FPGA design flows. Simulation results for a set of benchmark circuits show that the scheme is capable of achieving high levels of security against Trojan attacks at modest area, power and delay overhead.
Combining Neural Networks and Log-linear Models to Improve Relation Extraction
(2015)

The last decade has witnessed the success of traditional feature-based methods for exploiting the discrete structures such as words or lexical patterns to extract relations from text. Recently, convolutional and recurrent neural networks have been shown to capture effective hidden structures within sentences via continuous representations, thereby significantly advancing the performance of relation extraction. The advantage of convolutional neural networks is their capacity to generalize the consecutive k-grams in the sentences while recurrent neural networks are effective to encode long range sentence context. This paper proposes to combine the traditional feature-based method, the convolutional and recurrent neural networks to simultaneously benefit from their advantages. Our systematic evaluation of different network architectures and combination methods demonstrates the effectiveness of this approach and results in the state-ofthe-art performance on the ACE 2005 and SemEval datasets.
A second generation computer forensic analysis system
(2009)

The architecture of existing – first generation – computer forensic tools, including the widely used EnCase and FTK products, is rapidly becoming outdated. Tools are not keeping pace with increased complexity and data volumes of modern investigations. This paper discuses the limitations of first generation computer forensic tools. Several metrics for measuring the efficacy and performance of computer forensic tools are introduced. A set of requirements for second generation tools are proposed. A high-level design for a (work in progress) second generation computer forensic analysis system is presented. a 2009 Digital Forensic Research workshop. Published by Elsevier Ltd. All rights reserved.
A Low-Power 32-Channel Digitally Programmable Neural Recording Integrated Circuit
(2011)

We report the design of an ultra-low-power 32-channel neural-recording integrated circuit (chip) in a 0.18 μ m CMOS technology. The chip consists of eight neural recording modules where each module contains four neural amplifiers, an analog multiplexer, an A/D converter, and a serial programming interface. Each amplifier can be programmed to record either spikes or LFPs with a programmable gain from 49-66 dB. To minimize the total power consumption, an adaptive-biasing scheme is utilized to adjust each amplifier's input-referred noise to suit the background noise at the recording site. The amplifier's input-referred noise can be adjusted from 11.2 μVrms (total power of 5.4 μW) down to 5.4 μVrms (total power of 20 μW) in the spike-recording setting. The ADC in each recording module digitizes the a.c. signal input to each amplifier at 8-bit precision with a sampling rate of 31.25 kS/s per channel, with an average power consumption of 483 nW per channel, and, because of a.c. coupling, allows d.c. operation over a wide dynamic range. It achieves an ENOB of 7.65, resulting in a net efficiency of 77 fJ/State, making it one of the most energy-efficient designs for neural recording applications. The presented chip was successfully tested in an in vivo wireless recording experiment from a behaving primate with an average power dissipation per channel of 10.1 μ W. The neural amplifier and the ADC occupy areas of 0.03 mm2 and 0.02 mm2 respectively, making our design simultaneously area efficient and power efficient, thus enabling scaling to high channel-count systems.
Linear-Time Algorithms for Testing the Satisfiability of Propositional Horn Formulae
(1984)

D New algorithms for deciding whether a (propositional) Horn formula is satisfiable are presented. If the Horn formula A contains K distinct propositional letters and if it is assumed that they are exactly Pi,. . . , PK, the two algorithms presented in this paper run in time O(N), where N is the total number of occurrences of literals in A. By representing a Horn proposition as a graph, the satisfiability problem can be formulated as a data flow problem, a certain type of pebbling. The difference between the two algorithms presented here is the strategy used for pebbling the graph. The first algorithm is based on the principle used for finding the set of nonterminals of a context-free grammar from which the empty string can be derived. The second algorithm is a graph traversal and uses a “call-by-need” strategy. This algorithm uses an attribute grammar to translate a propositional Horn formula to its corresponding graph in linear time. Our formulation of the satisfiability problem as a data flow problem appears to be new and suggests the possibility of improving efficiency using parallel processors. a
ECNUCS: Measuring Short Text Semantic Equivalence Using Multiple Similarity Measurements
(2013)

This paper reports our submissions to the Semantic Textual Similarity (STS) task in SemEval 2013 (Task 6). We submitted three Support Vector Regression (SVR) systems in core task, using 6 types of similarity measures, i.e., string similarity, number similarity, knowledge-based similarity, corpus-based similarity, syntactic dependency similarity and machine translation similarity. Our third system with different training data and different feature sets for each test data set performs the best and ranks 35 out of 90 runs. We also submitted two systems in typed task using string based measure and Named Entity based measure. Our best system ranks 5 out of 15 runs.
Protocol for developing ANN models and its application to the assessment of the quality of the ANN model development process in drinking water quality modelling
(2014)

The application of Artificial Neural Networks (ANNs) in the field of environmental and water resources modelling has become increasingly popular since early 1990s. Despite the recognition of the need for a consistent approach to the development of ANN models and the importance of providing adequate details of the model development process, there is no systematic protocol for the development and documentation of ANN models. In order to address this shortcoming, such a protocol is introduced in this paper. In addition, the protocol is used to critically review the quality of the ANN model development and reporting processes employed in 81 journal papers since 2000 in which ANNs have been used for drinking water quality modelling. The results show that model architecture selection is the best implemented step, while greater focus should be given to input selection considering input independence and model validation considering replicative and structural validity. 2014 Elsevier Ltd. All rights reserved.
From the ephemeral to the enduring: how approach-oriented mindsets lead to greater status.
(2013)

We propose that the psychological states individuals bring into newly formed groups can produce meaningful differences in status attainment. Three experiments explored whether experimentally created approach-oriented mindsets affected status attainment in groups, both immediately and over time. We predicted that approach-oriented states would lead to greater status attainment by increasing proactive behavior. Furthermore, we hypothesized that these status gains would persist longitudinally, days after the original mindsets had dissipated, due to the self-reinforcing behavioral cycles the approach-oriented states initiated. In Experiment 1, individuals primed with a promotion focus achieved higher status in their newly formed groups, and this was mediated by proactive behavior as rated by themselves and their teammates. Experiment 2 was a longitudinal experiment and revealed that individuals primed with power achieved higher status, both immediately following the prime and when the groups were reassembled 2 days later to work on new tasks. These effects were mediated by independent coders' ratings of proactive behavior during the first few minutes of group interaction. Experiment 3 was another longitudinal experiment and revealed that priming happiness led to greater status as well as greater acquisition of material resources. Importantly, these immediate and longitudinal effects were independent of the effects of a number of stable dispositional traits. Our results establish that approach-oriented psychological states affect status attainment, over and above the more stable characteristics emphasized in prior research, and provide the most direct test yet of the self-reinforcing nature of status hierarchies. These findings depict a dynamic view of status organization in which the same group may organize itself differently depending on members' incoming psychological states.
Relating reinforcement learning performance to classification performance
(2005)

We prove a quantitative connection between the expected sum of rewards of a policy and binary classification performance on created subproblems. This connection holds without any unobservable assumptions (no assumption of independence, small mixing time, fully observable states, or even hidden states) and the resulting statement is independent of the number of states or actions. The statement is critically dependent on the size of the rewards and prediction performance of the created classifiers.We also provide some general guidelines for obtaining good classification performance on the created subproblems. In particular, we discuss possible methods for generating training examples for a classifier learning algorithm.
Semantic Labeling of Aerial and Satellite Imagery
(2016)

Inspired by the recent success of deep convolutional neural networks (CNNs) and feature aggregation in the field of computer vision and machine learning, we propose an effective approach to semantic pixel labeling of aerial and satellite imagery using both CNN features and hand-crafted features. Both CNN and hand-crafted features are applied to dense image patches to produce per-pixel class probabilities. Conditional random fields (CRFs) are applied as a postprocessing step. The CRF infers a labeling that smooths regions while respecting the edges present in the imagery. The combination of these factors leads to a semantic labeling framework which outperforms all existing algorithms on the International Society of Photogrammetry and Remote Sensing (ISPRS) two-dimensional Semantic Labeling Challenge dataset. We advance state-of-the-art results by improving the overall accuracy to 88% on the ISPRS Semantic Labeling Contest. In this paper, we also explore the possibility of applying the proposed framework to other types of data. Our experimental results demonstrate the generalization capability of our approach and its ability to produce accurate results.
Are Faculty Members Ready? Individual Factors Affecting Knowledge Management Readiness in Universities
(2016)

Knowledge Management (KM) provides a systematic process to help in the creation, transfer and use of knowledge across the university, leading to increased productivity. While KM has been successfully used elsewhere, universities have been late in adopting it. Before a university can initiate KM, it needs to determine if it is ready for KM or not. Through a web-based survey sent to 1263 faculty members from 59 accredited Library and Information Science programs in universities across North America, this study investigated the e®ect of individual factors of trust, knowledge self-e±cacy, collegiality, openness to change and reciprocity on individual readiness to participate in a KM initiative, and the degree to which this a®ects perceived organisational readiness to adopt KM. 157 valid responses were received. Using structural equation modeling, the study found that apart from trust, all other factors positively a®ected individual readiness, which was found to a®ect organisational readiness. Findings should help universities identify opportunities and barriers before they can adopt KM. It should be a useful contribution to the KM literature, especially in the university context.
Overview of Virtual Reality Technologies
(2012)

The promise of being able to be inside another world might be resolved by Virtual Reality. It wouldn’t be naïve to assume that we will be able to enter and completely feel another world at some point in the future; be able to interact with knowledge and entertainment in a totally immersive state. Advancements are becoming more frequent, and with the recent popularity of technology in this generation, a lot of investment is being made. Prototypes of head displays that completely cover the user’s view and movement recognition which doesn’t need an intermediate device for input of data are already Virtual Reality devices available to developers and even to the public. From time to time, the way we interact with computers change, and virtual reality promises to make this interaction as real as possible. Although scenes like flying a jet or tank, are already tangible, another scenes, such as being able to feel the dry air of the Sahara in geography classes or feel the hard, cold scales of a dragon in a computer game seem to be in a long w a y f r o m n o w. H o w e v e r , t e c h n o l o g i c advancements and increase in the popularity of these technologies point to the possibility of such amazing scenes coming true.
Ranking with local regression and global alignment for cross media retrieval
(2009)

Rich multimedia content including images, audio and text are frequently used to describe the same semantics in E-Learning and Ebusiness web pages, instructive slides, multimedia cyclopedias, and so on. In this paper, we present a framework for cross-media retrieval, where the query example and the retrieved result(s) can be of different media types. We first construct Multimedia Correlation Space (MMCS) by exploring the semantic correlation of different multimedia modalities, during which multimedia content and co-occurrence information is utilized. We propose a novel ranking algorithm, namely ranking with Local Regression and Global Alignment (LRGA), which learns a robust Laplacian matrix for data ranking. In LRGA, for each data point, a local linear regression model is used to predict the ranking values of its neighboring points. We propose a unified objective function to globally align the local models from all the data points so that an optimal ranking value can be assigned to each data point. LRGA is insensitive to parameters, making it particularly suitable for data ranking. A relevance feedback algorithm is proposed to improve the retrieval performance. Comprehensive experiments have demonstrated the effectiveness of our methods.
Using Smart City Technology to Make Healthcare Smarter
(2018)

Smart cities use information and communication technologies (ICTs) to scale services include utilities and transportation to a growing population. In this paper, we discuss how smart city ICTs can also improve healthcare effectiveness and lower healthcare cost for smart city residents. We survey current literature and introduce original research to offer an overview of how smart city infrastructure supports strategic healthcare using both mobile and ambient sensors combined with machine learning. Finally, we consider challenges that will be faced as healthcare providers make use of these opportunities.